16 citations · 61 across the 37 of their papers we have counts for
9 papers · 1 filter
What makes a good metric? Evaluating automatic metrics for text-to-image consistency
Candace Ross, Melissa Hall, Adriana Romero Soriano +1
Language models are increasingly being incorporated as components in larger AI systems for various purposes, from prompt optimization to automatic evaluation. In this work, we anal…
Boosting Latent Diffusion with Perceptual Objectives
Tariq Berrada, Pietro Astolfi, Melissa Hall +6
Latent diffusion models (LDMs) power state-of-the-art high-resolution generative image models. LDMs learn the data distribution in the latent space of an autoencoder (AE) and produ…
On Improved Conditioning Mechanisms and Pre-training Strategies for Diffusion Models
Tariq Berrada Ifriqi, Pietro Astolfi, Melissa Hall +8
Large-scale training of latent diffusion models (LDMs) has enabled unprecedented quality in image generation. However, the key components of the best performing LDM training recipe…
Improving Geo-diversity of Generated Images with Contextualized Vendi Score Guidance
Reyhane Askari Hemmat, Melissa Hall, Alicia Sun +3
With the growing popularity of text-to-image generative models, there has been increasing focus on understanding their risks and biases. Recent work has found that state-of-the-art…
Decomposed evaluations of geographic disparities in text-to-image models
Abhishek Sureddy, Dishant Padalia, Nandhinee Periyakaruppa +6
Recent work has identified substantial disparities in generated images of different geographic regions, including stereotypical depictions of everyday objects like houses and cars.…
Consistency-diversity-realism Pareto fronts of conditional image generative models
Pietro Astolfi, Marlene Careil, Melissa Hall +5
Building world models that accurately and comprehensively represent the real world is the utmost aspiration for conditional image generative models as it would enable their use as…
Improving Text-to-Image Consistency via Automatic Prompt Optimization
Oscar Mañas, Pietro Astolfi, Melissa Hall +6
Impressive advances in text-to-image (T2I) generative models have yielded a plethora of high performing models which are able to generate aesthetically appealing, photorealistic im…
DP-RDM: Adapting Diffusion Models to Private Domains Without Fine-Tuning
Jonathan Lebensold, Maziar Sanjabi, Pietro Astolfi +4
Text-to-image diffusion models have been shown to suffer from sample-level memorization, possibly reproducing near-perfect replica of images that they are trained on, which may be…
GPS-SSL: Guided Positive Sampling to Inject Prior Into Self-Supervised Learning
Aarash Feizi, Randall Balestriero, Adriana Romero-Soriano +1
We propose Guided Positive Sampling Self-Supervised Learning (GPS-SSL), a general method to inject a priori knowledge into Self-Supervised Learning (SSL) positive samples selection…